Comparison of Akaike information criterion (AIC) and Bayesian information criterion (BIC) in selection of stock–recruitment relationships

Abstract Variations in environmental variables and measurement errors often result in large and heterogeneous variations in fitting fish stock–recruitment (SR) data to a SR statistical model. In this paper, the maximum likelihood method was used to fit the six statistical SR models on six sets of simulated SR data. The best relationships were selected using the Akaike information criterion (AIC) and Bayesian information criterion (BIC) methods, respectively. Which have the advantage of testing the significance of the difference between the functions of different model specifications. The exercises were also conducted on eight sets of real fisheries SR data. The results showed that both AIC and BIC are valid in selecting the most suitable SR relationship. As far as the nested models are concerned, BIC is better than AIC.

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